class_docstr: Class for preprocessing Time Series Forecasting Experiments.
class_docstr_tok: ["Class","for","preprocessing","Time","Series","Forecasting","Experiments","."]
docstr: Add feature engineering steps to the pipeline.
  NOTE: Only Applied to Reduced regression models (see note in Setup).
  But in these models, target feature engineering is done internal to the
  model. Hence target feature engineering is not done here.
  
  Parameters
  ----------
  fe_exogenous : Optional[list]
      Feature Engineering Transformer to apply to exogenous variables.
  exogenous_present : TSExogenousPresent
      TSExogenousPresent.YES if exogenous variables are present in the data,
      TSExogenousPresent.NO otherwise. Exogenous feature transformers are
      only added if this is set to TSExogenousPresent.YES.
docstr_tok: ["Add","feature","engineering","steps","to","the","pipeline",".","NOTE",":","Only","Applied","to","Reduced","regression","models","(","see","note","in","Setup",")",".","But","in","these","models",",","target","feature","engineering","is","done","internal","to","the","model",".","Hence","target","feature","engineering","is","not","done","here",".","Parameters","--","--","--","--","--","fe_exogenous",":","Optional","[","list","]","Feature","Engineering","Transformer","to","apply","to","exogenous","variables",".","exogenous_present",":","TSExogenousPresent","TSExogenousPresent.YES","if","exogenous","variables","are","present","in","the","data",",","TSExogenousPresent.NO","otherwise",".","Exogenous","feature","transformers","are","only","added","if","this","is","set","to","TSExogenousPresent.YES","."]
code: def _feature_engineering(
      self,
      fe_exogenous: Optional[list],
      exogenous_present: TSExogenousPresent,
  ):
      """Add feature engineering steps to the pipeline.
      NOTE: Only Applied to Reduced regression models (see note in Setup).
      But in these models, target feature engineering is done internal to the
      model. Hence target feature engineering is not done here.
  
      Parameters
      ----------
      fe_exogenous : Optional[list]
          Feature Engineering Transformer to apply to exogenous variables.
      exogenous_present : TSExogenousPresent
          TSExogenousPresent.YES if exogenous variables are present in the data,
          TSExogenousPresent.NO otherwise. Exogenous feature transformers are
          only added if this is set to TSExogenousPresent.YES.
      """
      # Transform exogenous variables ----
      # Only add exogenous pipeline steps if exogenous variables are present,
      # but this is an exception. We may not have exogenous variables, but
      # these could be created using fe_exogenous, so we do not explicitly check
      # for the presence of exogenous variables to add this to the pipeline.
      # if exogenous_present == TSExogenousPresent.YES and fe_exogenous is not None:
      if fe_exogenous is not None:
          self._add_feat_eng_steps(
              fe_exogenous=fe_exogenous,
              target=False,
          )
  
file_path: pycaret/pycaret/internal/preprocess/time_series/forecasting/preprocessor.py
imports: from typing import Optional, Union
  from sklearn.preprocessing import MaxAbsScaler, MinMaxScaler, RobustScaler, StandardScaler
  from sktime.transformations.compose import ColumnwiseTransformer
  from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  from sktime.transformations.series.boxcox import BoxCoxTransformer, LogTransformer
  from sktime.transformations.series.cos import CosineTransformer
  from sktime.transformations.series.exponent import ExponentTransformer, SqrtTransformer
  from sktime.transformations.series.impute import Imputer
  from pycaret.utils.time_series import TSExogenousPresent
label_desc: Use image node_id 7 for calling the TSForecastingPreprocessor obj's underlying member method code with example usage: obj._feature_engineering(fe_exogenous, exogenous_present) without return types